regression-js VS examples

Compare regression-js vs examples and see what are their differences.

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regression-js examples
2 143
927 7,742
- 1.2%
0.0 6.2
over 1 year ago 21 days ago
JavaScript Jupyter Notebook
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.


Posts with mentions or reviews of regression-js. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-09.
  • Data Science with JavaScript: What we've learned so far?
    7 projects | | 9 Sep 2021
  • Hal9: Data Science with JavaScript
    4 projects | /r/datascience | 9 Sep 2021
    Modeling: We are currently exploring this space so our findings are not final yet but let me share what we've found so far. TensorFlow.js is absolutely amazing, it provides a native port from TensorFlow to JavaScript with CPU, WebGL, WebAssembly and NodeJS backends. We were able to write an LSTM to do time series prediction, so far so good. However, we started hitting issues when we started to do simpler models, like a linear regression. We tried Regression.js but we found it falls short, so we wrote our own script to handle single-variable regressions using TF.js and gradient decent. It actually worked quite well but exposed a flaw in this approach; basically, there is a lot of work to be done to bring many models into the web. We also found out Arquero.js does not play nicely with TF.js since well, Arquero.js does not support tensors; so we went on to explore Danfo.js, which integrates great with TF.js but we found out it's hard to scale it's transformations to +1M rows and found other rough edges. Since then, well, we started exploring Pyodide and perhaps contributing to Danfo.js, or perhaps involve more server-side compute, but that will be a story for another time.


Posts with mentions or reviews of examples. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-04-23.
  • My Favorite DevTools to Build AI/ML Applications!
    9 projects | | 23 Apr 2024
    TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.
  • Open Source Ascendant: The Transformation of Software Development in 2024
    4 projects | | 19 Mar 2024
    AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [] and PyTorch []. This democratization of AI tools is driving innovation and lowering entry barriers across industries.
  • Best AI Tools for Students Learning Development and Engineering
    2 projects | | 18 Mar 2024
    Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework.
  • Releasing The Force Of Machine Learning: A Novice’s Guide 😃
    3 projects | | 22 Feb 2024
    TensorFlow: An open-source machine learning framework for high-performance numerical computations, especially well-suited for deep learning.
  • MLOps in practice: building and deploying a machine learning app
    2 projects | | 11 Jan 2024
    The tool used to build the model per se was TensorFlow, a very powerful and end-to-end open source platform for machine learning with a rich ecosystem of tools. And in order to to create the needed script using TensorFlow Jupyter Notebook was used, which is a web-based interactive computing platform.
  • 🔥14 Excellent Open-source Projects for Developers😎
    5 projects | | 10 Dec 2023
    10. TensorFlow - Make Machine Learning Work for You 🤖
  • GPU Survival Toolkit for the AI age: The bare minimum every developer must know
    1 project | | 12 Nov 2023
    AI models, particularly those built on deep learning frameworks like TensorFlow, exhibit a high degree of parallelism. Neural network training involves numerous matrix operations, and GPUs, with their expansive core count, excel in parallelizing these operations. TensorFlow, along with other popular deep learning frameworks, optimizes to leverage GPU power for accelerating model training and inference.
  • 🔥🚀 Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot 🤖💬
    17 projects | | 6 Nov 2023
    #2 TensorFlow
  • Are there people out there who still like Sam atlman - AI IS AT DANGER
    3 projects | /r/ChatGPT | 31 Oct 2023
  • Tensorflow help
    1 project | /r/FTC | 29 Oct 2023
    I am on a new ftc team trying to get vision to work. I used the ftc machine learning tool chain but I have yet to get a good result with at best a 10% accuracy rate. I have changed everything possible in the tool chain with little luck. To fix this, I have tried making my own .tflite model using the google colab from When ever I try to run the same code with my own .tflite model, it gives me the error "User code threw an uncaught exception: IllegalStateException - Error getting native address of native library: task_vision_jni". It gives me the same error with official tensor flow tflite test models, and when I put them on a raspberry pi, both worked just fine. Does anyone have a fix to this error or even just tips for the machine learning toolchain?

What are some alternatives?

When comparing regression-js and examples you can also consider the following projects:

arquero - Query processing and transformation of array-backed data tables.

cppflow - Run TensorFlow models in C++ without installation and without Bazel

dplyr - dplyr: A grammar of data manipulation

mlpack - mlpack: a fast, header-only C++ machine learning library

hal9ai - Hal9 — Data apps powered by code and LLMs [Moved to:]

awesome-teachable-machine - Useful resources for creating projects with Teachable Machine models + curated list of already built Awesome Apps!

Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

face-api.js - JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js

Keras - Deep Learning for humans

Selenium WebDriver - A browser automation framework and ecosystem.

Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration